where \(i\)  and \(j\)  denote 2D spatial positions of feature maps, respectively. As done in [1]}, we collect correlation maps computed from all the intermediate features of same spatial size and stack them to obtain a hypercorrelation \(\mathcal {C}_p = \lbrace \mathcal {C}^l\rbrace _{l\in \mathcal {L}_p} \in \mathbb {R}^{h_{q} \times w_{q} \times h_{s} \times w_{s} \times |\mathcal {L}_p|}\) , where \(h_{q}, w_{q}\)  and \(h_{s}, w_{s}\)  are height and width of feature maps of query and support, respectively, and \(\mathcal {L}_p\)  is subset of CNN layer indices \(\lbrace 1,...,L\rbrace \)  at some pyramid layer \(p\) , indicating the correlation maps of identical spatial size.
